# Use Nested Algorithms to Increase Scalability

One powerful way to increase the scalability of a flow graph is to nest other parallel algorithms inside of node bodies. Doing so, you can use a flow graph as a coordination language, expressing the most coarse-grained parallelism at the level of the graph, with finer grained parallelism nested within.

In the example below, five nodes are created: a source_node, matrix_source, that reads a sequence of matrices from a file, two function_nodes, n1 and n2, that receive these matrices and generate two new matrices by applying a function to each element, and two final function_nodes, n1_sink and n2_sink, that process these resulting matrices. The matrix_source is connected to both n1 and n2. The node n1 is connected to n1_sink, and n2 is connected to n2_sink. In the lambda expressions for n1 and n2, a parallel_for is used to apply the functions to the elements of the matrix in parallel. The functions read_next_matrix, f1, f2, consume_f1 and consume_f2 are not provided below.

```    graph g;
source_node< double * > matrix_source( g, [&]( double * &v ) -> bool {
if ( a ) {
v = a;
return true;
} else {
return false;
}
}, false );
function_node< double *, double * > n1( g, unlimited, [&]( double *a ) -> double * {
double *b = new double[N];
parallel_for( 0, N, [&](int i) {
b[i] = f1(a[i]);
} );
return b;
} );
function_node< double *, double * > n2( g, unlimited, [&]( double *a ) -> double * {
double *b = new double[N];
parallel_for( 0, N, [&](int i) {
b[i] = f2(a[i]);
} );
return b;
} );
function_node< double *, double * > n1_sink( g, unlimited,
[]( double *b ) -> double * {
return consume_f1(b);
} );
function_node< double *, double * > n2_sink( g, unlimited,
[]( double *b ) -> double * {
return consume_f2(b);
} );
make_edge( matrix_source, n1 );
make_edge( matrix_source, n2 );
make_edge( n1, n1_sink );
make_edge( n2, n2_sink );
matrix_source.activate();
g.wait_for_all();
```